Skip to Content


No results


No results


No results

Search Results


No results
Search events using: keywords, sponsors, locations or event type
When / Where
All occurrences of this event have passed.
This listing is displayed for historical purposes.

Presented By: DCMB Seminar Series

DCMB / CCMB Weekly Seminar

Hatice Ülkü Osmanbeyoğlu, PhD, "Machine learning for single-cell regulatory program inference"

Signaling-regulated transcription factors (TFs) orchestrate the developmental and differentiation trajectories of cells and their activation states. Understanding TF activities at the single-cell level represents a formidable challenge. Single-cell multi-omics technologies now measure different modalities such as RNA, surface proteins, and chromatin states. Moreover, emerging spatial technologies offer highly multiplex profiling of RNAs and proteins, while preserving the spatial context of the tissue. Consequently, there is a tremendous need for computational methods to integrate these measurements and infer the underlying cell type- and state-specific transcriptional programs. In this talk, I will present our computational frameworks for delineating context-specific regulatory programs based on single-cell omics and spatial transcriptomics datasets. These frameworks have the potential to fill a significant gap in knowledge by defining cell context-specific regulators driving cellular identity, as well as discovering new targets and approaches for advancing therapy.

Short Bio:
Hatice Ülkü Osmanbeyoğlu joined University of Pittsburgh in December 2018 as an Assistant Professor in the Department of Biomedical Informatics at the University of Pittsburgh School of Medicine and a member of Hillman's Cancer Biology Program. She is also affiliated with the Intelligent System Program in the School of Computing and Information, the Bioengineering in the School of Medicine and the Biostatistics in the School of Public Health. Hatice has a BS in Computer Engineering from Northeastern University (2004, Summa Cum Laude), MS in Electrical and Computer Engineering from Carnegie Mellon University (2006), MS in Bioengineering from University of Pittsburgh (2009) and a Ph.D. in Biomedical Informatics from University of Pittsburgh (2012). She was a postdoctoral fellow in the Christina Leslie Lab at Memorial Sloan Kettering Cancer Center. Her multidisciplinary research utilizes novel machine learning techniques and multi-omics and imaging technologies to extract therapeutical and biological insight for the purpose of precision medicine. She is a recipient of the Memorial Sloan Kettering Postdoctoral Research Award, NIH NCI Pathway to Independence Award, the Innovation in Cancer Informatics Award and Maximizing Investigators’ Research Award (MIRA) for Early Stage Investigators (R35).

Back to Main Content